Can the global uptake of palliative care innovations be improved? Insights from a bibliometric analysis of the Edmonton Symptom Assessment System
Bibliographic record
Abstract
Clinical research is undertaken to improve care for palliative patients, but little is known about how to support the broad uptake of resultant innovations. The objectives of this paper are to: (1) explore the uptake of the Edmonton Symptom Assessment System throughout the global palliative care community through the lens of a bibliometric review - a research method that maps out the journey of new knowledge uptake by evaluating where key articles are cited in published literature; (2) construct hypotheses on attributes of the global community of palliative care learners; and (3) make inferences on approaches that could improve knowledge transfer. While preliminary, results of the study suggest several specific approaches that could support widespread uptake of innovations in palliative care: targeting publication in high impact, international journals; explicitly focusing on how the innovation is applied to best practice; encouraging additional research to expand on early studies; consciously targeting key professional groups and organizations to promote discussion in the grey literature; and early translation and promotion within multiple languages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.231 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.076 | 0.140 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".